DocumentCode :
3455453
Title :
Relevance Feedback Algorithm Based on Memory Support Vector Machines
Author :
Sun, Shu-Liang ; Wang, Shou-Jue
Author_Institution :
Dept. of Electron. & Inf. Eng., Tong Ji Univ., Shanghai, China
fYear :
2010
fDate :
21-23 Oct. 2010
Firstpage :
1
Lastpage :
5
Abstract :
Support vector machine(SVM) is based on the minimum of structure risk and used for small samples in machine learning. Memory support vector machine(MSVM) feedback is based on SVM and used cumulation samples replacing feedback samples by memory. It reduces the risk of recall vibration. MSVM feedback also proposes memory label which is used for lightening user´s burden. MSVM feedback is proved its superiority by relevant experiments.
Keywords :
learning (artificial intelligence); relevance feedback; support vector machines; machine learning; memory support vector machines; relevance feedback algorithm; structure risk; Conferences; Electronic mail; Head; Magnetic heads; Radio frequency; Support vector machines; Vibrations;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (CCPR), 2010 Chinese Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-7209-3
Electronic_ISBN :
978-1-4244-7210-9
Type :
conf
DOI :
10.1109/CCPR.2010.5659122
Filename :
5659122
Link To Document :
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